Compared to other platforms such as Coursera and EdX, FutureLearn is a relatively new player in the MOOC arena and received limited coverage in the Learning Analytics and Educational Data Mining research. Founded by a partnership between the Open University in the UK, the BBC, The British Library and (originally) 12 universities in the UK, FutureLearn has two distinctive features relevant to the way their data is displayed and analyzed: 1) it was designed with a specific educational philosophy in mind which focuses on the social dimension of learning and 2) every learning activity provide opportunities for formal discussion and commenting. This workshop provides an opportunity to invite contributions and connect individual and groups to share their research activities on an international stage. As the first of its kind, this workshop will bring in a number of scholars and practitioners, as well as data scientists and analyst involved in the reporting, researching and developments emerging from the data offered by the platform.

Collaborative filtering recommender systems often use nearest neighbor methods to identify candidate items. In this paper we present an inverted neighborhood model, k-Furthest Neighbors, to identify less ordinary neighborhoods for the purpose of creating more diverse recommendations. The approach is evaluated two-fold, once in a traditional information retrieval evaluation setting where the model is trained and validated on a split train/test set, and once through an online user study (N=132) to identify users' perceived quality of the recommender. A standard k-nearest neighbor recommender is used as a baseline in both evaluation settings. Our evaluation shows that even though the proposed furthest neighbor model is outperformed in the traditional evaluation setting, the perceived usefulness of the algorithm shows no significant difference in the results of the user study.

Existing semantic representations of music analysis encapsulate narrow sub-domain concepts and are frequently scoped by the context of a particular MIR task. Segmentation is a crucial abstraction in the investigation of phenomena which unfold over time; we present a Segment Ontology as the backbone of an approach that models properties from the musicological domain independently from MIR implementations and their signal processing foundations, whilst maintaining an accurate and complete description of the relationships linking them. This framework provides two principal advantages which we explore through several examples: a layered separation of concerns that aligns the model with the needs of the users and systems that consume and produce the data; and the ability to link multiple analyses of differing types through transforms to and from the Segment axis.

The world of music is changing rapidly. We are now just a few clicks away from being able to listen to nearly any song that has ever been recorded. This easy access to a nearly endless supply of music is changing how we explore, discover, share and experience music

This paper presents an extensive analysis of a sample of a social network of musicians. The network sample is first analyzed using standard complex network techniques to verify that it has similar properties to other web-derived complex networks. Content-based pairwise dissimilarity values between the musical data associated with the network sample are computed, and the relationship between those content-based distances and distances from network theory explored. Following this exploration, hybrid graphs and distance measures are constructed, and used to examine the community structure of the artist network. Finally, results of these investigations are shown to be mostly orthogonal between these distance spaces. These results are considered with a focus recommendation and discovery applications employing these hybrid measures as their basis.

We present a proof-of-concept system that demonstrates the utility of linked data for enhancing the application of Music Information Retrieval (MIR) workflows, both when curating collections of music signal data for analysis, and publishing results that can be simply and readily correlated to these, and other, collection sets and Linked Data sources. The system includes: linked data implementations of a signal repository, collection builder, and results explorer, an extension to the my Experiment workflow sharing environment to include Meandre workflows, and support within my Experiment and Meandre to retrieve and persist resources from the linked data repositories. By way of example we gather and publish RDF describing signal collections derived from the country of an artist. Genre analysis over these collections and integration of collection and result metadata enables us to ask: "how country is my country?".